Hence, in this survey, we provide a comprehensive evaluation study of state-of-the-art algorithms by taking into account all the mentioned aspects (1) to (4). As summarized in Table REF , standard performance metrics, as well as the novel performance metrics proposed in [1]} are considered. In addition, the number of parameters of DL-based approaches is reported as it directly impacts the memory consumption and the model scalability. Moreover, experiments according to the nature of anomalies are carried out using the taxonomy that was recently introduced in [2]}. Lastly, a unified experimental protocol is used to compare existing methods. This in-depth evaluation study will help the community understand the advantages and limitations of state-of-the-art techniques from a broader applicative perspective and lay the foundations for better experimental evaluation practices.
